Improving Operational Efficiency

With a growing digital market and increasing global competition, companies are being challenged to become more operationally efficient to remain competitive. At the same time, the world is becoming better at turning data into insights. Massive opportunities have come from innovations in cloud, edge, and Big Data technology enabling firms to gather, process, and analyze information quickly.
While a lot of the highlighted and publicized benefits of Big Data are seen in marketing, technology, or AI-based start-ups, many are starting to understand how it can also make them more efficient. In this post we will look at some of the ways operators are using data to unlock efficiencies, increase productivity, reduce costs and deliver great service.   

What is Big Data?
In short, Big Data is defined as incredibly large datasets that can be analyzed computationally and systematically to reveal trends, patterns and associations in data from multiple sources. A lot of investment is going into Big Data as firms are realizing how data can be used to make better business decisions and strategic moves. 

What do we mean by operational efficiency?
You probably hear the term thrown around a lot and it’s quite easy to pass by as buzz or hype rather than anything you should care about. Operational efficiency refers to the capability of a business to deliver its products or services in the most cost-effective way possible without compromising on product quality, support offerings, or technology.
There are many ways it can be applied to your business when driven by Big Data.

Automating Business Processes
It is quite common for businesses that have been established for a long time to rely on manual and resource heavy processes in their day-to-day operations. This doesn’t mean it is possible to take everything your business does and automate it using Big Data, but an iterative process can be very effective. For example, you might find a manual process for which simple elements of data can be inserted to make it quicker. A good case for this is with teams who are sending letters to customers and typing out the addresses for each. A Big Data process can automate adding addresses to letters. Once comfortable with that, it might even be possible to create a procedure that prints the letters or automatically sends them to a third party. Iteratively, the laborious manual process becomes fully automated.

Optimizing Resource
Managing the workforce has always been a tough job for any business. There has also been a thin line between not having enough staff and having too many staff for specific tasks. A prime example is telephony staff in a call center. Traditionally, call center managers have gone on gut feel to work out how many people they need available to answer phones each day. However, with Big Data, there is an opportunity to optimize their placement of resources.
Businesses can collect data on the volume of phone calls, website traffic, external influences, staff shifts, transactions and much more to understand how customers behave. With this information, they can more accurately predict the number of staff they need to have available at every minute of every day. As more data is ingested, they become more accurate over time.

Internal Risk Control
A common problem raised by business leaders is the inability to process large volumes of data from multiple sources. Often, legacy systems don’t have the computing power and infrastructure to make that happen. As the data overwhelms internal controls, it creates operational, financial and reputational risks. There is a lot of pressure on regulators and management to identify risks quickly and to act on them proactively. The likes of General Data Protection Regulation (GDPR) have set down very specific rules for data that, when not followed, could lead to hefty fines and brand damage.
Big Data is being used to improve how data is managed and stored, create detailed audit trails, case management and accurate reporting. The objective here is to try to negate and associated business risks.

Delivery and Shipping
Shipping of products is one area that can benefit greatly from Big Data. Companies who leverage Big Data can gather data from many sources such as road traffic, routes, weather and temperature. All these sources companies can be analyzed to provide a much better estimate of the time taken to delivery good or services. There are two major benefits of this. First, the customer is satisfied in the knowledge they know exactly when they will receive their item. Second, businesses can optimize the routes their drivers take to optimize the time they are on the road, ultimately reducing the cost of services.

Supply Chain Management
The fourth industrial revolution, referred to an industry 4.0, is having a major effect on the efficiencies of supply chain management and manufacturing. A lot of the area Industry 4.0 encompasses focus on the use of Big Data.
Within a supply chain, there can be several stakeholders. Big Data connects all the parties involved by supporting Internet of Things (IoT) devices in an integrated network. For example, if there is an anomaly with packaging, data insights can notify other parties and take an appropriate action. This could mean pausing delivery or production. After that, appropriate adjustments can be made automatically that get the chain back on track.

Product development
When developing a new product, firms can go through substantial and time-consuming trial and error processes to get them right. Furthermore, this could end up being very costly. For example, if a development team releases a product that doesn’t work as consumers would expect, they have gone to a lot of expense for little return.
Big Data can take the guess work out of product development and accurately propose what is possible. This can take in consumer data, product data, competitor information and much more to derive the best possible product. This can go as far as optimizing the materials used in product creation. For example, if a company uses Material A, Big Data analysis might reveal that using Material B is more cost effective whilst working just as well as Material B. It can help businesses make very quick informed decisions.

Summary
Whilst Data Science has been penned as one of the sexiest jobs of the 21st century, we would be lying if we told you that investing into enhanced analytics will command photo ops or grab headlines. However, what it will do is unlock huge potential for businesses to become more efficient, reduce their costs, improve productivity and remain competitive. It is vital that modern organizations make the most of all the sources available to them and take a grip on Big Data.